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Conformal Prediction

Conformal Prediction is a machine learning framework that provides valid measures of confidence for individual predictions. It offers a principled approach to quantify uncertainty in predictions without assuming any specific distribution for the data. This section features papers that explore various aspects of conformal prediction, including theoretical advancements, algorithmic developments, and applications across different domains.

Papers

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Showing 491–500 of 704 papers

TitleStatusHype
Training-Aware Risk Control for Intensity Modulated Radiation Therapies Quality Assurance with Conformal Prediction—0
On the Out-of-Distribution Coverage of Combining Split Conformal Prediction and Bayesian Deep Learning—0
Training-Conditional Coverage Bounds for Uniformly Stable Learning Algorithms—0
On the Temperature of Bayesian Graph Neural Networks for Conformal Prediction—0
Training-Conditional Coverage Bounds under Covariate Shift—0
On the Utility of Prediction Sets in Human-AI Teams—0
On the Validity of Conformal Prediction for Network Data Under Non-Uniform Sampling—0
On Training-Conditional Conformal Prediction and Binomial Proportion Confidence Intervals—0
Training conformal predictors—0
On Uncertainty In Natural Language Processing—0
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